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3D‐SHOTGUN: A novel, cooperative, fold‐recognition meta‐predictor
Author(s) -
Fischer Daniel
Publication year - 2003
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.10357
Subject(s) - shotgun , computer science , server , shotgun sequencing , similarity (geometry) , artificial intelligence , fold (higher order function) , shotgun proteomics , protein structure prediction , pattern recognition (psychology) , machine learning , data mining , protein structure , biology , genome , proteomics , image (mathematics) , genetics , biochemistry , world wide web , gene , programming language
Abstract To gain a better understanding of the biological role of proteins encoded in genome sequences, knowledge of their three‐dimensional (3D) structure and function is required. The computational assignment of folds is becoming an increasingly important complement to experimental structure determination. In particular, fold‐recognition methods aim to predict approximate 3D models for proteins bearing no sequence similarity to any protein of known structure. However, fully automated structure‐prediction methods can currently produce reliable models for only a fraction of these sequences. Using a number of semiautomated procedures, human expert predictors are often able to produce more and better predictions than automated methods. We describe a novel, fully automatic, fold‐recognition meta‐predictor, named 3D‐SHOTGUN, which incorporates some of the strategies human predictors have successfully applied. This new method is reminiscent of the so‐called cooperative algorithms of Computer Vision. The input to 3D‐SHOTGUN are the top models predicted by a number of independent fold‐recognition servers. The meta‐predictor consists of three steps: (i) assembly of hybrid models, (ii) confidence assignment, and (iii) selection. We have applied 3D‐SHOTGUN to an unbiased test set of 77 newly released protein structures sharing no sequence similarity to proteins previously released. Forty‐six correct rank‐1 predictions were obtained, 30 of which had scores higher than that of the first incorrect prediction—a significant improvement over the performance of all individual servers. Furthermore, the predicted hybrid models were, on average, more similar to their corresponding native structures than those produced by the individual servers. This opens the possibility of generating more accurate, full‐atom homology models for proteins with no sequence similarity to proteins of known structure. These improvements represent a step forward toward the wider applicability of fully automated structure‐prediction methods at genome scales. Proteins 2003;51:434–441. © 2003 Wiley‐Liss, Inc.